Online or onsite, instructor-led live AutoML training courses demonstrate through interactive hands-on practice the fundamentals and advanced concepts of AutoML.
AutoML training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live AutoML training can be carried out locally on customer premises in Boston or in NobleProg corporate training centers in Boston.
NobleProg -- Your Local Training Provider
MA, Boston - Federal Street
101 Federal Street Suite 1900, Boston, united states, 02110
The office in Boston's Federal Street is a recently renovated office complex set in the heart of the financial district, just one block from Congress Street. Based on the 16th and 19th floors, the office offers spectacular views of the city. Finance and tourism are some of the prime businesses in the state capital, which is the largest city in New England. With its many colleges and universities, Boston is also regarded an international center ...
The office in Boston's Federal Street is a recently renovated office complex set in the heart of the financial district, just one block from Congress Street. Based on the 16th and 19th floors, the office offers spectacular views of the city. Finance and tourism are some of the prime businesses in the state capital, which is the largest city in New England. With its many colleges and universities, Boston is also regarded an international center of higher education, with students contributing more than $4billion to the economy annually. It has a strong reputation for medicine, biotechnology and research. It is ranked in the top 20 of global financial centers and number one for innovation. Boston also has an historic seaport which supports tourism, industry and fishing.
This instructor-led, live training in Boston (online or onsite) is aimed at participants with varying levels of expertise who wish to leverage Google's AutoML platform to build customized chatbots for various applications.
By the end of this training, participants will be able to:
Understand the fundamentals of chatbot development.
Navigate the Google Cloud Platform and access AutoML.
Prepare data for training chatbot models.
Train and evaluate custom chatbot models using AutoML.
Deploy and integrate chatbots into various platforms and channels.
Monitor and optimize chatbot performance over time.
This instructor-led, live training in Boston (online or onsite) is aimed at technical persons with a background in machine learning who wish to optimize the machine learning models used for detecting complex patterns in big data.
By the end of this training, participants will be able to:
Install and evaluate various open source AutoML tools (H2O AutoML, auto-sklearn, TPOT, TensorFlow, PyTorch, Auto-Keras, TPOT, Auto-WEKA, etc.)
Train high quality machine learning models.
Efficiently solve different types of supervised machine learning problems.
Write just the necessary code to initiate the automated machine learning process.
This instructor-led, live training in Boston (online or onsite) is aimed at data scientists, data analysts, and developers who wish to explore AutoML products and features to create and deploy custom ML training models with minimal effort.
By the end of this training, participants will be able to:
Explore the AutoML product line to implement different services for various data types.
Prepare and label datasets to create custom ML models.
Train and manage models to produce accurate and fair machine learning models.
Make predictions using trained models to meet business objectives and needs.
This instructor-led, live training in Boston (online or onsite) is aimed at data scientists and data analysts who wish to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities.
By the end of this training, participants will be able to:
Load datasets in DataRobot to analyze, assess, and quality check data.
Build and train models to identify important variables and meet prediction targets.
Interpret models to create valuable insights that are useful in making business decisions.
Monitor and manage models to maintain an optimized prediction performance.
This instructor-led, live training in Boston (online or onsite) is aimed at technical persons who wish to build machine learning models using algorithms such as GLM, Deep Learning and Random Forests.
By the end of this training, participants will be able to:
Install and configure H2O.
Create machine learning models using different popular algorithms.
Evaluate models based on the type of data and business requirements.
This instructor-led, live training in Boston (online or onsite) is aimed at data scientists who wish to use H2O AutoML to automoate the process of building and selecting the best machine learning algorithm and parameters.
By the end of this training, participants will be able to:
Automate the machine learning workflow.
Automatically train and tune many machine learning models within a specified time range.
Train stacked ensembles to arrive at highly predictive ensemble models.
This instructor-led, live training in Boston (online or onsite) is aimed at machine learning practitioners who wish to use Auto-sklearn to automate the process of selecting and optimizing a machine learning model.
By the end of this training, participants will be able to:
Automate the process of training highly efficient machine learning models.
Build highly accurate machine learning models while bypassing the more tedious tasks of selecting, training and testing different models.
Use the power of machine learning to solve real-world business problems.
This instructor-led, live training in Boston (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model.
By the end of this training, participants will be able to:
Automate the process of training highly efficient machine learning models.
Automatically search for the best parameters for deep learning models.
Build highly accurate machine learning models.
Use the power of machine learning to solve real-world business problems.
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